Directional modifier adaptation based on input selection for real-time optimization

Abstract

Modifier adaptation (MA) is commonly used for economic optimization of systems under model uncertainty. In MA, gradient correction terms require estimation through perturbations, thus delaying the optimization procedure. A directional modifier adaptation method is proposed whereby a subset of available gradient corrections are made. An algorithm that evaluates possible adaptation strategies and chooses those with the largest economic effect is proposed, thereby allowing economical operation with less delay. The proposed scheme, named dMAIS, is deployed on the Williams-Otto process where it is found to outperform MA if not inhibited by filtering. Systems can also suffer from constraint violation if uncertainty is present, hampering safety and profitability. An adjustment step is proposed as part of dMAIS, whereby gradients are used to drive the plant to constraint satisfaction. The adjustments are studied in an evaporator case with a product quality constraint whereby dMAIS is shown to result in infrequent violations leading to higher throughput. The proposed approach was also compared to standard directional MA in the evaporator case study, where it was found to be economically beneficial. The benefits of dMAIS are observed most saliently for systems with increasing disturbance frequency.